Laying the Groundwork for Reliable Automotive AI: Best Practices for Sensor Setup and Data Collection - aiMotive
Laying the Groundwork for Reliable Automotive AI: Best Practices for Sensor Setup and Data Collection
Written by Zsófia Ülkei, Zoltán Nagy, Frida Kóbor, Péter Tamás Kovács, Bence Boda / Posted at 8 August 2025
Developing automotive AI
Developing automotive AI – whether for ADAS or automated driving – starts with one critical foundation: data. High-quality, precise, and well-documented data enables everything from neural network training, validation and simulation testing.
But here’s the catch: data collection can also be the most expensive part of the entire AI development lifecycle. You need vehicles, sensors, drivers, custom software, and countless hours of engineering. So, it's surprising how often this investment is not used to its full potential – either because of preventable mistakes or the lack of understanding of how the recorded data will be used due to communication mismatch.
At aiMotive, we’ve seen too many teams burn through budgets only to discover months later that their collected data isn’t usable. The reasons vary: sensor misplacement, bad calibration, poor version control, or missing diagnostic metadata. This creates a domino effect: development slows, debugging becomes harder, and trust in the data crumbles.
We created aiData to help developers avoid this. In this first post in our new technical blog series, we’ll walk through the essential best practices we’ve learned from supporting real-world programs across continents. From setting up your sensor stack to diagnostics and calibration, here’s what you need to get right from day one.
1. Create a robust sensor setup
Before you collect a single byte, your sensor configuration must be rock solid.
- Simulate your sensor layout before physically installing anything. Check for blind spots, overlapping FOVs, and range limitations.
- Avoid placing GT sensors on moving parts like side mirrors or trunk lid– this can undermine calibration stability.
- Use rigid mounting structures and remain consistent with your design. One well-planned design should serve you for multiple data collection campaigns to avoid blowing your budget. We also usually advise against heavy, metal structures due to their increased weight and associated safety risks, as well as possible shielding and interference effects. As an alternative, aiMotive uses custom AI-optimized, 3D-printed, reinforced mounts.
- Plan for tomorrow’s data needs. Equip your vehicles with high-quality GT sensors from the start, to ensure that the setup is designed to support evolving and future needs.
- Validate sensor integration, consider which parts of the pipeline will be affected by the addition of new sensors, and ensure to allocate sufficient time for proper validation.
aiMotive’s aiSim can simulate your sensor setup virtually, so you can validate coverage, optimize placement, and avoid surprises once the car hits the road. In addition, aiMotive is happy to support customers in designing the ideal sensor setup catering to various requirements and use-cases.
2. Get calibration & synchronization right
Sensors are only as good as their alignment and timing. If calibration or sync is off, your dataset becomes a liability.
- Define calibration tolerances based on the use-case. Don't assume every program and use-case needs the same accuracy – aim for the most stringent planned use case.
- Continuously check calibration health – ideally with an automated tool that runs during or after every drive.
- Version every calibration change to make debugging traceable and to manage accidental drift with precise validity intervals for calibration data.
- Prioritize selecting hardware-sync-capable sensors wherever possible and include diagnostics to verify timing on the fly.
aiData automates these checks and flags anomalies before they become costly problems. Moreover, our teams have extensive experience in helping clients resolve their calibration issues and advising on suitable methods for error-free data collection drives.
3. Configure your recording software smartly
Even the most perfect sensor setup won’t help if the recording software fails.
- Record raw data, not post-processed outputs – this ensures future reprocessing is possible.
- Log everything: not just sensor output, but system state, GPS, diagnostic flags, operator notes, and environmental conditions.
With aiData, you can control sensor parameters, automatically create metadata, and ensure consistent, traceable data streams from a single interface.
4. Enable real-time feedback for drivers
The aiData recording software is equipped with a real-time driver dashboard for monitoring data collection status.
You don’t want to discover three weeks later that a critical sensor failed halfway through a 500 km route.
- Show live system health on a driver dashboard, so drivers immediately know if something’s wrong. Make sure that the dashboard is clearly visible to the driver and provides straightforward visual clues for quick error detection.
- Implement on-the-fly diagnostics with clear thresholds for flags like sensor overheating, occlusion, or sync loss.
aiData supports live sensor diagnostics so that the driver is notified immediately if intervention is required during the drive. By this, aiData ensures that no recording is going to waste.
5. Optimize data upload and management
Even the best quality data loses its value if it’s difficult to access or filter
- Enable processing pipelines to kick off immediately with automatic uploads – don’t wait days or weeks for data to reach engineers, deploy upload stations right where vehicles finish their data collection trips.
- Utilize automatic flags for faster processing, collected during the data collection drives, if a Software-under-Test (SuT) is connected. The SuT knows best when it’s confidence is lower then expected, and these moments can be filtered and prioritized.
- Track diagnostic trends and thresholds over time to spot hardware issues before they escalate.
With aiData, your recorded data, including diagnostic information, is uploaded, processed, and indexed automatically, keeping your teams agile and informed.
Conclusion
High-quality data is not a given; it's a result of rigorous design, disciplined execution, and the right tooling.
At aiMotive, we’ve seen firsthand how aiData helps OEMs and Tier-1s avoid costly mistakes, speed up development, and build AI that works – in the lab, in simulation, and on real roads.
This blog is the first in a series covering how to build a scalable, reliable, and high-fidelity automotive data pipeline. Stay tuned, and if you’re struggling with any part of this, get in touch. We’re happy to help.